Update app.py
Browse files
app.py
CHANGED
@@ -10,8 +10,15 @@ from pathlib import Path
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import random
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import torchvision.transforms as transforms
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model = load_learner('export (2).pkl')
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def transform_image(image):
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my_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalzie([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])
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@@ -19,12 +26,14 @@ def transform_image(image):
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def predict(img):
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img = PILImage.create(img)
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image = transforms.Resize((480,640))(img)
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tensor = transform_image(image=image)
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model.to(device)
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with torch.no_grad():
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outputs = model(tensor)
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mask = np.array(outputs.cpu())
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mask[mask==0]=255
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mask[mask==1]=150
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@@ -32,6 +41,7 @@ def predict(img):
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mask[mask==3]=25
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mask[mask==4]=0
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mask=np.reshape(mask,(480,640))
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Image.fromarray(mask.astype('uint8'))
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import random
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import torchvision.transforms as transforms
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import PIL
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import gradio as gr
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = load_learner('export (2).pkl')
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model.cpu()
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model.eval()
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def transform_image(image):
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my_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalzie([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])
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def predict(img):
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img = PILImage.create(img)
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image = transforms.Resize((480,640))(img)
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tensor = transform_image(image=image)
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with torch.no_grad():
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outputs = model(tensor)
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outputs = torch.argmax(outputs,1)
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mask = np.array(outputs.cpu())
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mask[mask==0]=255
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mask[mask==1]=150
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mask[mask==3]=25
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mask[mask==4]=0
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mask=np.reshape(mask,(480,640))
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return Image.fromarray(mask.astype('uint8'))
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gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128,128)), outputs=gr.inputs.Image(), examples=['color_157.jpg','color_158.jpg']).launch(share=False)
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